Since the inception of computers, random numbers have played important roles in areas such as Monte Carlo simulations, probabilistic computing methods (simulated annealing, genetic algorithms, neural networks, and the like), computer-based gaming, and very large scale integration (VLSI) chip-testing. The bulk of the investigation into random (more properly, pseudo-random) number generation methods has been centered around arithmetic algorithms. This is because the prevalent computing medium has been the general purpose, arithmetic computer. Digital hardware designers have long relied on feedback shift registers to generate random numbers.
With the advent of VLSI design, built-in self-tests have become advantageous. In this design, the bulk of the chip testing system is incorporated on the chip itself. Linear feedback shift registers were used initially to implement the random number generation portion of the built-in self-test.
In 1986, Wolfram (S. Wolfram, “Random sequence generation by cellular automata,” Advances in Applied Mathematics, vol. 7, pp. 123–169, June 1986) described a random sequence generation by a simple one-dimensional (1-d) cellular automata with a neighborhood size of three. The work focused on the properties of a particular CA-based RNG identified as “CA30,” so named due to the decimal value of its truth table. Statistical tests indicated that the CA30 was a superior random number generator to the ones based on linear feedback shift registers. Wolfram suggested that efficient hardware implementation of the CA30 should be possible.
Hortensius et al. (P. D. Hortensius, R. D. McLeod, and H. C. Card, “Parallel number generation for VLSI systems using cellular automata,” IEEE Transactions on Computers, vol. 38, no. 10, pp. 1466–1473, October 1989) described the use of CA30 as a random number generator in an Ising computer. They also described using combinations of CAs (CA90 and CA150), which generated even better random numbers than the CA30. They further indicated that time and site spacing may improve statistical quality of random numbers generated by the CA. Time spacing is where the RNG is advanced more than one step between random number samples and site spacing is where not every bit value generated is used.
In the above conventional methods, no systematic way of fabricating a high quality CA-based RNGs exists.